Multiple imputation (MI) [1]} has increasingly become a popular tool for analyses with missing data in medical research [2]}, [3]}; the method is now incorporated in many standard statistical software packages.[4]}, [5]}, [6]} In MI, several completed datasets are created and in each, missing data are replaced with values drawn from an imputation model which is the Bayesian posterior predictive distribution of the missing data, given the observed data. Each completed dataset is then analysed using the substantive analysis model that would have been used had there been no missing data. This process generates several sets of parameter estimates, which are then combined into a single set of results using Rubin's rules.[1]}, [8]} Given congenial specification of the imputation model, Rubin's rules provide estimates of standard errors and confidence intervals that correctly reflect the uncertainty introduced by missing data.
